# HLA B44 motif neoepitopes in NSCLC: Evaluating their effects on the TME and adding them to established markers in a model to predict durable benefit from PD- 1 inhibition with and without chemotherapy

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2024 · $225,068

## Abstract

PROJECT SUMMARY
Lung cancer is the leading cause of cancer related deaths in the United States and the World. We recently
demonstrated that programmed cell death 1 (PD-1) inhibitors, which lead to durable responses in a minority of
non-small cell lung cancer (NSCLC) patients, have greater efficacy in patients with charged HLA-B binding
pockets whose tumors harbor mutation(s) leading to what we have designated as motif neoepitopes. Motif
neoepitopes have an amino acid substitution in the second position of a nonamer generating a change in charge
from the wild type peptide with the resultant amino acid having a charge opposite from the HLA-B binding pocket.
To date, the immunological changes induced by motif neoepitopes have not been explored. We propose a
comprehensive evaluation of the underlying mechanism, focusing on patients with HLA-B44 supertype alleles
because of the prevalence (approximately 40% of the population) and distribution of HLA-B44 across racial and
ethnic groups. We will evaluate samples in the cancer genome atlas (TCGA) from patients with at least one HLA-
B44 supertype allele to explore differences in the tumor microenvironment (TME) among patients with or without
motif neoepitopes by examining gene expression and cellular composition by slide review and algorithms based
on gene expression profiles. We will evaluate surgical specimens from treatment naïve patients with or without
HLA-B44 motif neoepitopes and evaluate spatial signatures of the TME by multiplex immunofluorescence (MIF).
We will assess multiple sections from each specimen to identify biomarkers most significantly associated with
motif neoepitopes. We will further examine immune contextures of the TME associated with motif neoepitopes
by single cell RNA-seq analysis.
To elucidate the predictive value of motif neoepitopes in early stage NSCLC patients, we will perform whole
exome sequencing (WES) and transcriptomic data, which we now routinely obtain in our NSCLC patients as part
of patients' clinical care. We will analyze the presence and expression of genes harboring motif neoepitopes.
Together, these studies will provide a better understanding of the TME and other immunologic changes
associated with the presence of motif neoepitopes.
Dr. Velez is trained as a medical oncologist, and her true goal is to bring innovative clinical trials to representative
populations and perform correlative analyses on these representative populations. She has learned a great deal
about stastitical analysis as part of her training to date. However, she has not spent much time working on
modern techniques to analyze biospecimens. Analysis of biospecimens are generally what provide junior
investigators with the hypotheses that can be translated into innovative clinical trials. That is certainly the case
in one of my prior trainees, who currently has a K08 and recently submitted his first R01 application. I believe
that with Dr. Velez' participation in this research project, she will...

## Key facts

- **NIH application ID:** 10994035
- **Project number:** 3R01CA276917-02S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** EDWARD B GARON
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $225,068
- **Award type:** 3
- **Project period:** 2023-08-01 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10994035

## Citation

> US National Institutes of Health, RePORTER application 10994035, HLA B44 motif neoepitopes in NSCLC: Evaluating their effects on the TME and adding them to established markers in a model to predict durable benefit from PD- 1 inhibition with and without chemotherapy (3R01CA276917-02S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10994035. Licensed CC0.

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